Learning geometric annotations such as an optical flow that relies substantially on motion parameters (rather than textures) have been studied extensively in the past decade. Unlike monocular depth estimation and instance segmentation, the prediction of such geometric quantities through convolutional neural networks generalizes well across different domains. For such predictions, synthetic datasets have widely been used [1]}, [2]}, [3]}, [4]} with a limited number of objects and textures. Recent advancements in scalable scene generation environments like Kubric [5]} have shown improvements in optical flow prediction due to more variability in data generation.
